Я большой специалист в области искусственного интеллекта, я попытался обучить модель начального уровня 4 с 10 классами. я просмотрел код, чтобы проверить, где были допущены несоответствия размеров, затем изменил эти части в соответствии со своими потребностями, я также нашел проблему и обратился к ChatGPT за возможным исправлением, но ничего не помогло.
вот какую ошибку я получаю:
ValueError: The shape of the target variable and the shape of the target value in `variable.assign(value)` must match. variable.shape=(1536, 10), Received: value.shape=(1536, 1001). Target variable:
это первоначальная настройка кода — никаких изменений здесь не вносилось
#inception v4 code for detection and classification
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def conv_block(x, nb_filter, nb_row, nb_col, padding='same', strides=(1, 1), use_bias=False):
if K.image_data_format() == "th":
channel_axis = 1
else:
channel_axis = -1
x = Conv2D(nb_filter, (nb_row, nb_col), strides=strides, padding=padding, use_bias=use_bias)(x)
x = BatchNormalization(axis=channel_axis)(x)
x = Activation('relu')(x)
return x
print ("pls work 2")
def inception_stem(input):
if K.image_data_format() == "th":
channel_axis = 1
else:
channel_axis = -1
# Input Shape is 299 x 299 x 3 (th) or 3 x 299 x 299 (th)
x = conv_block(input, 32, 3, 3, strides=(2, 2), padding='valid')
x = conv_block(x, 32, 3, 3, padding='valid')
x = conv_block(x, 64, 3, 3)
x1 = MaxPooling2D((3, 3), strides=(2, 2), padding='valid')(x)
x2 = conv_block(x, 96, 3, 3, strides=(2, 2), padding='valid')
x = concatenate([x1, x2], axis=channel_axis)
x1 = conv_block(x, 64, 1, 1)
x1 = conv_block(x1, 96, 3, 3, padding='valid')
x2 = conv_block(x, 64, 1, 1)
x2 = conv_block(x2, 64, 1, 7)
x2 = conv_block(x2, 64, 7, 1)
x2 = conv_block(x2, 96, 3, 3, padding='valid')
x = concatenate([x1, x2], axis=channel_axis)
x1 = conv_block(x, 192, 3, 3, strides=(2, 2), padding='valid')
x2 = MaxPooling2D((3, 3), strides=(2, 2), padding='valid')(x)
x = concatenate([x1, x2], axis=channel_axis)
return x
print ("pls work 3")
def inception_A(input):
if K.image_data_format() == "th":
channel_axis = 1
else:
channel_axis = -1
a1 = conv_block(input, 96, 1, 1)
a2 = conv_block(input, 64, 1, 1)
a2 = conv_block(a2, 96, 3, 3)
a3 = conv_block(input, 64, 1, 1)
a3 = conv_block(a3, 96, 3, 3)
a3 = conv_block(a3, 96, 3, 3)
a4 = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(input)
a4 = conv_block(a4, 96, 1, 1)
m = concatenate([a1, a2, a3, a4], axis=channel_axis)
return m
def inception_B(input):
if K.image_data_format() == "th":
channel_axis = 1
else:
channel_axis = -1
b1 = conv_block(input, 384, 1, 1)
b2 = conv_block(input, 192, 1, 1)
b2 = conv_block(b2, 224, 1, 7)
b2 = conv_block(b2, 256, 7, 1)
b3 = conv_block(input, 192, 1, 1)
b3 = conv_block(b3, 192, 7, 1)
b3 = conv_block(b3, 224, 1, 7)
b3 = conv_block(b3, 224, 7, 1)
b3 = conv_block(b3, 256, 1, 7)
b4 = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(input)
b4 = conv_block(b4, 128, 1, 1)
m = concatenate([b1, b2, b3, b4], axis=channel_axis)
return m
def inception_C(input):
if K.image_data_format() == "th":
channel_axis = 1
else:
channel_axis = -1
c1 = conv_block(input, 256, 1, 1)
c2 = conv_block(input, 384, 1, 1)
c2_1 = conv_block(c2, 256, 1, 3)
c2_2 = conv_block(c2, 256, 3, 1)
c2 = concatenate([c2_1, c2_2], axis=channel_axis)
c3 = conv_block(input, 384, 1, 1)
c3 = conv_block(c3, 448, 3, 1)
c3 = conv_block(c3, 512, 1, 3)
c3_1 = conv_block(c3, 256, 1, 3)
c3_2 = conv_block(c3, 256, 3, 1)
c3 = concatenate([c3_1, c3_2], axis=channel_axis)
c4 = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(input)
c4 = conv_block(c4, 256, 1, 1)
m = concatenate([c1, c2, c3, c4], axis=channel_axis)
return m
def reduction_A(input):
if K.image_data_format() == "th":
channel_axis = 1
else:
channel_axis = -1
r1 = conv_block(input, 384, 3, 3, strides=(2, 2), padding='valid')
r2 = conv_block(input, 192, 1, 1)
r2 = conv_block(r2, 224, 3, 3)
r2 = conv_block(r2, 256, 3, 3, strides=(2, 2), padding='valid')
r3 = MaxPooling2D((3, 3), strides=(2, 2), padding='valid')(input)
m = concatenate([r1, r2, r3], axis=channel_axis)
return m
def reduction_B(input):
if K.image_data_format() == "th":
channel_axis = 1
else:
channel_axis = -1
r1 = conv_block(input, 192, 1, 1)
r1 = conv_block(r1, 192, 3, 3, strides=(2, 2), padding='valid')
r2 = conv_block(input, 256, 1, 1)
r2 = conv_block(r2, 256, 1, 7)
r2 = conv_block(r2, 320, 7, 1)
r2 = conv_block(r2, 320, 3, 3, strides=(2, 2), padding='valid')
r3 = MaxPooling2D((3, 3), strides=(2, 2), padding='valid')(input)
m = concatenate([r1, r2, r3], axis=channel_axis)
return m
print ("pls work 5")
проблема, похоже, возникает в этой части, где число предварительно обученных было 1001, а у меня 10, поэтому я применил изменения в этом сегменте, и именно здесь возникает ошибкаdef create_inception_v4(nb_classes=10, load_weights=True):
'''
Creates an Inception v4 network
:param nb_classes: number of classes
:return: Keras Model with 1 input and 1 output
'''
if K.image_data_format() == 'th':
init = Input((3, 299, 299))
else:
init = Input((299, 299, 3))
# Input Shape is 299 x 299 x 3 (tf) or 3 x 299 x 299 (th)
x = inception_stem(init)
# 4 x Inception A
for i in range(4):
x = inception_A(x)
# Reduction A
x = reduction_A(x)
# 7 x Inception B
for i in range(7):
x = inception_B(x)
# Reduction B
x = reduction_B(x)
# 3 x Inception C
for i in range(3):
x = inception_C(x)
# Average Pooling
x = AveragePooling2D((8, 8))(x)
# Dropout
x = Dropout(0.8)(x)
x = Flatten()(x)
# Output
out = Dense(units=10, activation='softmax')(x)
model = Model(init, out, name='Inception-v4')
if load_weights:
if K.backend() == "theano":
if K.image_data_format() == "th":
weights = get_file('inception_v4_weights_th_dim_ordering_th_kernels.h5', TH_BACKEND_TH_DIM_ORDERING,
cache_subdir='models')
else:
weights = get_file('inception_v4_weights_tf_dim_ordering_th_kernels.h5', TH_BACKEND_TF_DIM_ORDERING,
cache_subdir='models')
else:
if K.image_data_format() == "th":
weights = get_file('inception_v4_weights_th_dim_ordering_tf_kernels.h5', TF_BACKEND_TH_DIM_ORDERING,
cache_subdir='models')
else:
weights = get_file('inception_v4_weights_tf_dim_ordering_tf_kernels.h5', TH_BACKEND_TF_DIM_ORDERING,
cache_subdir='models')
model.load_weights(weights)
print("Model weights loaded.")
return model
if __name__ == "__main__":
# from keras.utils.visualize_util import plot
inception_v4 = create_inception_v4(load_weights=True)
# inception_v4.summary()
print ("pls work 1")
# plot(inception_v4, to_file="Inception-v4.png", show_shapes=True)
Подробнее здесь: https://stackoverflow.com/questions/784 ... on-4-model
